Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001). Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system’s accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.

Mortality from COVID-19 among kidney transplant recipients (KTR) is high, and their response to three vaccinations against SARS-CoV-2 is strongly impaired. We retrospectively analyzed the serological response of up to five doses of the SARS-CoV-2 vaccine in KTR from 27 December 2020 until 31 December 2021. Particularly, the influence of the different dose adjustment regimens for mycophenolic acid (MPA) on serological response to fourth vaccination was analyzed. In total, 4277 vaccinations against SARS-CoV-2 in 1478 patients were analyzed. Serological response was 19.5% after 1203 basic immunizations, and increased to 29.4%, 55.6%, and 57.5% in response to 603 third, 250 fourth, and 40 fifth vaccinations, resulting in a cumulative response rate of 88.7%. In patients with calcineurin inhibitor and MPA maintenance immunosuppression, pausing MPA and adding 5 mg prednisolone equivalent before the fourth vaccination increased the serological response rate to 75% in comparison to the no dose adjustment (52%) or dose reduction (46%). Belatacept-treated patients had a response rate of 8.7% (4/46) after three vaccinations and 12.5% (3/25) after four vaccinations. Except for belatacept-treated patients, repeated SARS-CoV-2 vaccination of up to five times effectively induces serological response in kidney transplant recipients. It can be enhanced by pausing MPA at the time of vaccination.

We picture an 18-year-old young man from a village somewhere in Europe. His name is Omer. Omer has been suffering from disease episodes since his early childhood: bursts of fever, fatigue and abdominal pain. He visited many doctors, but none of them was able to establish a satisfactory treatment. He went through a diagnostic and therapeutic odyssey typical for rare disease patients. Fatigue has led him into social isolation. He is perceived as a malingerer.1

Mortality from COVID-19 among kidney transplant recipients (KTR) is high, and their response to three vaccinations against SARS-CoV-2 is strongly impaired. We retrospectively analyzed serological response of up to five doses of SARS-CoV-2 vaccine in KTR from December 27, 2020, until December 31, 2021. Particularly, the influence of different dose adjustment regimens for mycophenolic acid (MPA) on serological response to fourth vaccination was analyzed. In total, 4.277 vaccinations against SARS-CoV-2 in 1.478 patients were analyzed. Serological response was 19.5% after 1.203 basic immunizations, and increased to 29.4%, 55.6%, and 57.5% in response to 603 third, 250 fourth and 40 fifth vaccinations, resulting in a cumulative response rate of 88.7%. In patients with calcineurin inhibitor and MPA maintenance immunosuppression, pausing MPA and adding 5 mg prednisolone equivalent before the fourth vaccination increased serological response rate to 75% in comparison to no dose adjustment (52%) or dose reduction (46%). Belatacept-treated patients had a response rate of 8.7% (4/46) after three vaccinations and 12.5% (3/25) after four vaccinations. Except for belatacept-treated patients, repeated SARS-CoV-2 vaccination of up to five times effectively induces serological response in kidney transplant recipients. It can be enhanced by pausing MPA at the time of vaccination.

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.

The Banff 2017 report permits the diagnosis of pure chronic antibody-mediated rejection (cAMR) in absence of microcirculation inflammation. We retrospectively investigated renal allograft function and long-term outcomes of 67 patients with cAMR, and compared patients who received antihumoral therapy (cAMR-AHT, n = 21) with patients without treatment (cAMRwo, n = 46). At baseline, the cAMR-AHT group had more concomitant T-cell-mediated rejection (9/46 (19.2%) vs. 10/21 (47.6%); p = 0.04), a higher g-lesion score (0.4 ± 0.5 versus 0.1 ± 0.3; p = 0.01) and a higher median eGFR decline in the six months prior to biopsy (6.6 vs. 3.0 mL/min; p = 0.04). The median eGFR decline six months after biopsy was comparable (2.6 vs. 4.9 mL/min, p = 0.61) between both groups, and three-year graft survival after biopsy was statistically lower in the cAMR-AHT group (35.0% vs. 61.0%, p = 0.03). Patients who received AHT had more infections (0.38 vs. 0.20 infections/patient; p = 0.04). Currently, antihumoral therapy is more often administered to patients with cAMR and rapidly deteriorating renal function or concomitant TCMR. However, long-term graft outcomes remain poor, despite treatment.